I think you might be interested in learning about Amdahl's law: https://en.wikipedia.org/wiki/Amdahl%27s_law https://en.wikipedia.org/wiki/Amdahl%27s_law Some

I think you might be interested in learning about Amdahl's law: https://en.wikipedia.org/wiki/Amdahl%27s_law https://en.wikipedia.org/wiki/Amdahl%27s_law Some parts of an algorithm are parallelizable, but only up to a certain limit; and that's where the endgame bottleneck is: the last remaining 'in series' parts and the communication overhead for the parallelized parts. Note that their Dojo system has absolutely massive networking equipment: that's for having a better endgame at Amdahl's law. ---- On another note, one can modify the algorithm itself so that is has more inherent parallelism. For example DNA sequencing: you match short reads, them send them off to be reconciled together. The way to have more parallelism is to generate more matches -by lowering the threshold for matching-. Having more matches means more communication costs, but better parallelism. For gradient descent, one of the tricks is learning in batches of input data: the neural network doesn't learn from the freshest point of view, but it can be done in parallel (also the way to aggregate new knowledge is to do an average of the weights, which must limit the amount of things learned)